About this Research Topic
Sustainable urban built environments are increasingly critical as global urbanization accelerates, leading to higher population densities and expanded urban construction. These developments pose significant challenges in environmental sustainability, transportation, human behavior, safety, health, and resource management. Recent studies have highlighted the potential of intelligent and big data technologies to address these challenges and align with Sustainable Development Goals (SDGs), particularly those aimed at creating inclusive, safe, resilient, and sustainable urban areas. Despite notable progress in analytics, modeling, and urban planning, a critical gap remains in the holistic integration of these technologies to achieve comprehensive sustainability. Current research often focuses on isolated aspects, with limited attention to integrated, people-centric approaches considering health, well-being, carbon neutrality, and pollution reduction.
This Research Topic aims to explore the status quo of urban pollution control, advanced knowledge, modeling techniques, and methods for sustainable development. It also delves into policies, urban smart control challenges, and prospects for creating sustainable cities and societies. By addressing these areas, the Topic seeks to answer critical questions about how intelligent and big data technologies can be effectively integrated into urban planning and management to promote sustainability. Key questions include evaluating the effectiveness of digital twins in urban environments, the impact of coordinated management on urban heat islands and pollution, and examining the role of intelligent operations and maintenance in achieving low-carbon urban renewal.
To gather further insights into the sustainable urban built environment, we welcome articles addressing, but not limited to, the following themes:
• Intelligent sensing, modeling, and digital twin for urban environments
• Coordinated management of urban heat islands and pollution
• Urban traffic pollution control and management
• Blue-green infrastructure
• Low-carbon renewal and intelligent operations and maintenance for sustainable buildings
• Urban human mobility, spatial analysis, and city planning
• Green and sustainable development of megacities
This Research Topic aims to explore the status quo of urban pollution control, advanced knowledge, modeling techniques, and methods for sustainable development. It also delves into policies, urban smart control challenges, and prospects for creating sustainable cities and societies. By addressing these areas, the Topic seeks to answer critical questions about how intelligent and big data technologies can be effectively integrated into urban planning and management to promote sustainability. Key questions include evaluating the effectiveness of digital twins in urban environments, the impact of coordinated management on urban heat islands and pollution, and examining the role of intelligent operations and maintenance in achieving low-carbon urban renewal.
To gather further insights into the sustainable urban built environment, we welcome articles addressing, but not limited to, the following themes:
• Intelligent sensing, modeling, and digital twin for urban environments
• Coordinated management of urban heat islands and pollution
• Urban traffic pollution control and management
• Blue-green infrastructure
• Low-carbon renewal and intelligent operations and maintenance for sustainable buildings
• Urban human mobility, spatial analysis, and city planning
• Green and sustainable development of megacities
Keywords: Urban heat island, Pollution in urban environment, Intelligent sensing, Low-carbon and sustainable development, Sustainable urban built environment, Safety and health
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
